Electronic records, such as files and database entries, may be stored and utilized by an enterprise. Moreover, an enterprise may be interested in analyzing information about each electronic record to determine if a supplemental review process should be performed for that particular record. For example, the enterprise might want to identify which electronic records would most benefit from such a supplemental review process. Manually analyzing a batch of electronic records (e.g., each associated with a potential new risk association with a different entity) to identify which ones might most benefit from the supplemental review process, however, can be a time consuming and error prone process—especially where there are a substantial number of records to be analyzed (e.g., thousands of new electronic records might need to be analyzed each week while available resources might only allow a relatively small number of those records to be reviewed) and/or there are many factors that could potentially influence whether or not each record would benefit from the supplemental review process.
It would be desirable to provide systems and methods to automatically utilize a decision making model that generates faster, more accurate identifications of electronic records for a supplemental review process and that allows for flexibility and effectiveness when reviewing those identifications.